{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "cf9a11e5",
   "metadata": {},
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "3410558f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "d7aadb5f336549d2a1bb1115581eb6bf",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/11 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "最近 2 年上涨月数/总月数：11/25 | 概率：44.00%\n",
      "最近 3 年上涨月数/总月数：21/37 | 概率：56.76%\n",
      "最近 5 年上涨月数/总月数：31/61 | 概率：50.82%\n",
      "最近 8 年上涨月数/总月数：43/97 | 概率：44.33%\n",
      "最近 10 年上涨月数/总月数：55/121 | 概率：45.45%\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1200x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import akshare as ak\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import datetime\n",
    "import numpy as np\n",
    "\n",
    "# ========== 参数设置 ==========\n",
    "symbol = symbol_code = \"游戏\"\n",
    "year_spans = [2, 3, 5, 8, 10]  # 不同周期\n",
    "\n",
    "# ========== 获取完整数据 ==========\n",
    "today = datetime.datetime.today()\n",
    "full_start_date = (today - pd.DateOffset(years=max(year_spans))).strftime('%Y%m%d')\n",
    "end_date = today.strftime('%Y%m%d')\n",
    "\n",
    "df = ak.stock_board_industry_index_ths(symbol=symbol_code, start_date=full_start_date, end_date=end_date)\n",
    "df[\"日期\"] = pd.to_datetime(df[\"日期\"])\n",
    "df = df.sort_values(\"日期\")\n",
    "\n",
    "# ========== 初始化图表数据容器 ==========\n",
    "bar_data = {}  # key: N_YEARS, value: 月份 -> 上涨次数\n",
    "\n",
    "# ========== 收集每个周期的数据 ==========\n",
    "for N_YEARS in year_spans:\n",
    "    start_cut = today - pd.DateOffset(years=N_YEARS)\n",
    "    df_cut = df[df[\"日期\"] >= start_cut].copy()\n",
    "\n",
    "    # 月份字段\n",
    "    df_cut[\"月份\"] = df_cut[\"日期\"].dt.month\n",
    "\n",
    "    # 每月开盘收盘\n",
    "    monthly_df = df_cut.groupby(df_cut[\"日期\"].dt.to_period(\"M\")).agg({\n",
    "        \"开盘价\": \"first\",\n",
    "        \"收盘价\": \"last\"\n",
    "    }).reset_index()\n",
    "\n",
    "    monthly_df[\"月份\"] = monthly_df[\"日期\"].dt.month\n",
    "    monthly_df[\"上涨\"] = monthly_df[\"收盘价\"] > monthly_df[\"开盘价\"]\n",
    "\n",
    "    up_counts = monthly_df.groupby(\"月份\")[\"上涨\"].sum()\n",
    "    total_counts = monthly_df.groupby(\"月份\")[\"上涨\"].count()\n",
    "    up_probs = up_counts / total_counts\n",
    "\n",
    "    total_up_count = up_counts.sum()\n",
    "    total_months = len(monthly_df)\n",
    "    overall_up_probability = total_up_count / total_months if total_months else 0\n",
    "\n",
    "    print(f\"最近 {N_YEARS} 年上涨月数/总月数：{total_up_count}/{total_months} | 概率：{overall_up_probability:.2%}\")\n",
    "\n",
    "    bar_data[N_YEARS] = up_counts\n",
    "\n",
    "# ========== 合成一个图表：分组柱状图 ==========\n",
    "plt.figure(figsize=(12, 6))\n",
    "\n",
    "bar_width = 0.12\n",
    "months = np.arange(1, 13)\n",
    "offsets = np.linspace(-bar_width * len(year_spans) / 2, bar_width * len(year_spans) / 2, len(year_spans))\n",
    "\n",
    "for i, N_YEARS in enumerate(year_spans):\n",
    "    counts = bar_data.get(N_YEARS, pd.Series(0, index=range(1, 13)))\n",
    "    # 确保每月都有值\n",
    "    counts = counts.reindex(range(1, 13), fill_value=0)\n",
    "    plt.bar(months + offsets[i], counts.values, width=bar_width, label=f\"{N_YEARS}年\")\n",
    "\n",
    "plt.xticks(months, [f\"{m}月\" for m in months], rotation=-90)\n",
    "plt.xlabel(\"月份\")\n",
    "plt.ylabel(\"上涨次数\")\n",
    "plt.title(f\"{symbol}行业各周期每月上涨次数比较\")\n",
    "plt.legend(title=\"周期长度\")\n",
    "plt.grid(axis='y', linestyle='--', alpha=0.6)\n",
    "plt.tight_layout()\n",
    "plt.show()\n"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.11.0"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}
